
is a penalty function that enforces the priors that regularize the source parameters, and \(\lambda \)  is the
“regularization strength,” which controls the relative contributions of the two terms in Equation REF .
When \(\lambda \)  is large, \(E_s(\mathbf {s})\)  prevents the source from overfitting to the data by forcing it to be smooth;
when \(\lambda \)  is small, the source becomes more complex in order to better fit the data. For computational
efficiency, the penalty function must be expressible as a quadratic form as in Equation REF , and
the regularization matrix \(\mathbf {R}\)  must be both symmetric and invertible.
(This approach to fitting is known generally as Tikhonov regularization [1]} or ridge regression, although
applications in most other contexts often use \(\mathbf {R}\equiv \mathbf {I}\) .)
These conditions restrict our choice of a new regularization matrix in Section
REF .
